1 The Pandemic and the Unemployment Insurance

1.1 The beginning

In March 2020 the direction of the country began to change. The COVID-19 Pandemic raged through the country and lockdowns began. The result of this was a near standstill of the economy. As consumer demand decreased and businesses shut down, labor markets began to feel the heat. The combination of economic impacts and an increase in workplace hazards resulted in a massive spike in the unemployment rate.

The national rate reached a previously unseen level of 14.7 percent in April 2020. In response to this, Congress passed the CARES Act which contained provisions for enhanced and extended unemployment insurance benefits. While necessary for many to keep afloat during the economic crisis, a potential drawback of this policy is that workers may be disincentivized to work. If the benefit level is sufficiently high, returning to work may pay less money than remaining on unemployment insurance. A rational actor would see this and decide to stay on unemployment insurance and not return to the workforce.

The key question to address then becomes: Which population segment in the United States of America have seen more population disincentivization to return to work due to the Unemployment Insurance under COVID-19 after evaluating weekly data since 2020?

1.2 Why Pooled Cross section data is relevant?

Pooled cross section is a data structured where random samples are obtained from the population at different points in time. Pooled Cross Sections are obtained by collecting random samples from a large population independently at different points in time. Having random samples collected independently implies that they need not be of equal size and will usually contain different statistical units at different points in time.Therefore, limiting serial correlation of residuals when regression analysis is applied. The previous is useful for a variety of purposes, including policy analysis. For example, to study the effects of an unexpected change in unemployment insurance we can choose the treatment group to be unemployed individuals from a state that has a change in unemployment compensation, while a control group could be unemployed workers from a neighboring state. Since each observation is independent, the regression model of pooled cross section is as followed:

1.3 About the data

Since April 2020, the U.S. Census Bureau, in collaboration with multiple federal agencies, held the Household Pulse Survey(HPS). The main purpose of the survey was to be able to deploy quickly and efficiently, collected data to be able to measure household experiences during the COVID-19 pandemic.

The Survey consist of a 20-minute, online survey which is meant to assess how individuals, families, and communities are being affected by the COVID-19 pandemic. Participants are drawn from randomly selected addresses, and respondents may be asked to complete the survey up to three times to track the effect of the pandemic on households over time.The survey collects data from an average of around 97,000 respondents per week. The Survey continues measuring how the COVID-19 pandemic is impacting households across the US from a social and economic perspective. The phases were planned. Results for Phase 1 were released weekly, and results from Phases 2 and 3 were released every two weeks. The details of each data collection was as followed.

  • Data collection for Phase 1 of the Household Pulse Survey began on April 23, 2020 and ended on July 21, 2020.
  • Data collection for Phase 2 of the Household Pulse Survey began on August 19, 2020 and ended October 26, 2020.
  • Data collection for Phase 3 of the Household Pulse Survey began on October 28, 2020 and ended March 29, 2021.
  • Data collection for Phase 3.1 of the Household Pulse Survey began on April 14, 2021 and ended on July 5, 2021.
  • Data collection for Phase 3.2 of the Household Pulse Survey began July 21, 2021 and ended on October 11, 2021.
  • Data collection for Phase 3.3 of the Household Pulse Survey began December 1, 2021 and ended on February 7, 2022.
  • Data collection for Phase 3.4 of the Household Pulse Survey started on March 2, 2022 and ended on May 9, 2022.
  • Data collection for Phase 3.5 of the Household Pulse Survey started on June 1, 2022 and ended on August 8, 2022.
  • Data collection for Phase 3.6 of the Household Pulse Survey started on September 14, 2022 and is scheduled to continue until November 14, 2022. Phase 3.6 will continue with a two-weeks on, two-weeks off collection and dissemination approach, with data releases scheduled for October 5, October 26, and November 23, 2022.

The details for the dataset can be found in the following link: https://www.census.gov/programs-surveys/household-pulse-survey/datasets.html.

A complementary dataset that was also analyzed and incorporated was the Unemployment Insurance replacement rates from the Department of Labor of US available in the following link: https://oui.doleta.gov/unemploy/ui_replacement_rates.asp

The Replacement Rate is the ratio of the claimants’ weekly benefit amount (WBA) to the claimants’ average weekly wage. Two key Replacement Ratios are available:

  • Replacement Ratio 1 = Weighted Average of: WBA / (Normal Hourly Wage x 40 Hrs.)
  • Replacement Ratio 2 = Ratio of:Weighted Average WBA / Weighted Average (Normal Hourly Wage x 40 Hrs.)

1.4 Reasons for using this dataset

The HPS provides many advantages in comparison to other datasets that have measured unemployment insurance(i.e: the unemployment insurance of the labor department). Among the advantages that we can mention are:

  • The high frequency of the data which allows to measure employment more carefully.
  • The large sample which allows to approach towards the reality of the population.
  • The information asked allowing to understand socio-economic and geographic differences.

Some of the initial variables we are using from the dataset include:

  • State
  • Sex
  • Education credentials
  • Person was working in the previous week
  • Hispanic origin
  • Race
  • Whether someone in their household experienced job loss since March 2020
  • Whether a person was working in the previous week
  • Home ownership and rental information
  • Income category
  • Unemployment benefit received

Additionally, using the average replacement ratio will allow to further understand the motivation of workers to either return to the labor market given that with only the unemployment insurance benefits they could not be able to truly alleviate severe economic hardship, particularly for low-wage workers.

We would now initiate our Exploratory Data Analysis (EDA) in the following section, considering that our previously stated research question is:

  • Specific: It provides the variables that will be evaluated, the periodicity, and scale at the State level.
  • Measurable The question will allow to quantify the impacts
  • Achievable It can be achieve in a relative short period of time (~2 months)
  • Relevant Extremely relevant given that it can provide key information for policy makers and reduce population vulnerability
  • Time-oriented Given that it is focus in a short data frame the results should be ready by November 14
#### Cleaning the data####
# Setting working directory
setwd('/Users/admin/Documents/GitHub/DS6101_G3/Data')

#import and merge all CSV files into one data frame

df_panel <- list.files(path='/Users/admin/Documents/GitHub/DS6101_G3/Data') %>% 
  lapply(read_csv) %>% 
  bind_rows 

# remove additional unnecessary rows

df_panel<- df_panel[ -c(26:50) ]
df_panel<- df_panel[ -c(26) ]


#creating the panel
df_panel <- df_panel[ -c(4:5)]
df_panel<- panel_data(df_panel, id = SCRAM, wave = WEEK)


#renaming columns
colnames(df_panel)[1:23]=c("ID","Week","State","Age","Hispanic","Other_race","Edu","Sex","p_hh","wrklossrv", 'anywork','kindwork','rsnnowrkrv','UI_apply', 'UI_recrv','UI_recvnow','house', 'income','SPND1','SPND2','SPND3','SPND4','SPND5' )

#Changing birth year to age
df_panel$Age <- 2022-df_panel$Age

#Changing -99 and -88 for NAs
df_panel <- df_panel %>% dplyr::na_if(-99)
df_panel <- df_panel %>% dplyr::na_if(-88)

#Reconverting to panel data
df_panel2<- panel_data(df_panel, id = ID, wave = Week)
df_panel2

# Changing state names first run 
rep_str_state = c('10'='Delaware',
                  '11'='District of Columbia',
                  '12'='Florida',
                  '13'='Georgia',
                  '15'='Hawaii',
                  '16'='Idaho',
                  '17'='Illinois',
                  '18'='Indiana',
                  '19'='Iowa',
                  '20'='Kansas',
                  '21'='Kentucky',
                  '22'='Louisiana',
                  '23'='Maine',
                  '24'='Maryland',
                  '25'='Massachusetts',
                  '26'='Michigan',
                  '27'='Minnesota',
                  '28'='Mississippi',
                  '29'='Missouri',
                  '30'='Montana',
                  '31'='Nebraska',
                  '32'='Nevada',
                  '33'='New Hampshire',
                  '34'='New Jersey',
                  '35'='New Mexico',
                  '36'='New York',
                  '37'='North Carolina',
                  '38'='North Dakota',
                  '39'='Ohio',
                  '40'='Oklahoma',
                  '41'='Oregon',
                  '42'='Pennsylvania',
                  '44'='Rhode Island',
                  '45'='South Carolina',
                  '46'='South Dakota',
                  '47'='Tennessee',
                  '48'='Texas',
                  '49'='Utah',
                  '50'='Vermont',
                  '51'='Virginia',
                  '53'='Washington',
                  '54'='West Virginia',
                  '55'='Wisconsin',
                  '56'='Wyoming')

df_panel2$State<- str_replace_all(df_panel2$State,rep_str_state)

# Changing state names second run 
rep_str_state2 = c('1'='Alabama',
                  '2'='Alaska',
                  '4'='Arizona',
                  '5'='Arkansas',
                  '6'='California',
                  '8'='Colorado',
                  '9'='Connecticut')

df_panel2$State<- str_replace_all(df_panel2$State,rep_str_state2)

# removing NA's from ID column in dataframe

df_panel2<-df_panel2[!is.na(df_panel2$ID),]
df_panel2<-df_panel2[!is.na(df_panel2$State),]

#Adding mean replacement rate column by state and week

df_panel2$Week2[df_panel2$Week %in% 1:9]<- '1-9'
df_panel2$Week2[df_panel2$Week %in% 10:15]<- '10-15'
df_panel2$Week2[df_panel2$Week %in% 16:21]<- '16-21'
df_panel2$Week2[df_panel2$Week %in% 22:27]<- '22-27'
df_panel2$Week2[df_panel2$Week %in% 28:33]<- '28-33'
df_panel2$Week2[df_panel2$Week %in% 34:38]<- '34-38'
df_panel2$Week2[df_panel2$Week %in% 39:40]<- '39-40'
df_panel2$Week2[df_panel2$Week %in% 41:43]<- '41-43'
df_panel2$Week2[df_panel2$Week %in% 44:49]<- '44-49'

#Importing replacement rate data CSV
setwd('/Users/admin/Documents/GitHub/DS6101_G3')
rr<- read_csv("Replacement_rate.csv")

#Adding Mean replacement rate to data panel

df_panel3<- left_join(df_panel2, rr, by= c('Week2'='week','State'))
df_panel3

#removing additional unnecessary columns
df_panel3 <- as.data.frame(df_panel3)
df_panel3 <- df_panel3[ -c(19:24) ]



#copying df2 to df3
df_panel2<-df_panel3

#Changing categorical values for traditional 0 & 1    triple check this with dictionary
df_panel2$Hispanic[df_panel2$Hispanic==2]<- 0
df_panel2$Sex[df_panel2$Sex==2]<- 'Female' #Changing to categorical
df_panel2$Sex[df_panel2$Sex==1]<- 'Male'   #Changing to categorical

df_panel2$Edu[df_panel2$Edu==1]<- 'Less than high school' #Changing to categorical
df_panel2$Edu[df_panel2$Edu==2]<- 'Some high school' #Changing to categorical
df_panel2$Edu[df_panel2$Edu==3]<- 'High school grad or equiv' #Changing to categorical
df_panel2$Edu[df_panel2$Edu==4]<- 'Some college no degree received' #Changing to categorical
df_panel2$Edu[df_panel2$Edu==5]<-"Associate's degree" #Changing to categorical
df_panel2$Edu[df_panel2$Edu==6]<- "Bachelor's degree" #Changing to categorical
df_panel2$Edu[df_panel2$Edu==7]<- 'Graduate degree' #Changing to categorical

#df_panel2$income[df_panel2$income==1]<- 'Less than $25,000' #Changing to categorical
#df_panel2$income[df_panel2$income==2]<- '$25,000 - $34,999' #Changing to categorical
#df_panel2$income[df_panel2$income==3]<- '$35,000 - $49,999' #Changing to categorical
#df_panel2$income[df_panel2$income==4]<- '$50,000 - $74,999' #Changing to categorical
#df_panel2$income[df_panel2$income==5]<- '$75,000 - $99,999' #Changing to categorical
#df_panel2$income[df_panel2$income==6]<- '$100,000 - $149,999' #Changing to categorical
#df_panel2$income[df_panel2$income==7]<- '$150,000 - $199,999' #Changing to categorical
#df_panel2$income[df_panel2$income==8]<- '$200,000 and above' #Changing to categorical

df_panel2$income2[df_panel2$income==1]<- 'Less than $25,000' #Changing to categorical
df_panel2$income2[df_panel2$income==2]<- '$25,000 - $34,999' #Changing to categorical
df_panel2$income2[df_panel2$income==3]<- '$35,000 - $49,999' #Changing to categorical
df_panel2$income2[df_panel2$income==4]<- '$50,000 - $74,999' #Changing to categorical
df_panel2$income2[df_panel2$income==5]<- '$75,000 - $99,999' #Changing to categorical
df_panel2$income2[df_panel2$income==6]<- '$100,000 - $149,999' #Changing to categorical
df_panel2$income2[df_panel2$income==7]<- '$150,000 - $199,999' #Changing to categorical
df_panel2$income2[df_panel2$income==8]<- '$200,000 and above' #Changing to categorical

df_panel2$wrklossrv[df_panel2$wrklossrv==2]<- 0
df_panel2$anywork[df_panel2$anywork==2]<- 0
df_panel2$UI_apply [df_panel2$UI_apply ==2]<- 0
df_panel2$UI_recrv[df_panel2$UI_recrv==2]<- 0
df_panel2$UI_recvnow[df_panel2$UI_recvnow==2]<- 0

#Importing regions data CSV
setwd('/Users/admin/Documents/GitHub/DS6101_G3')
reg<- read_csv("US_regions.csv")

#Adding regions to data panel

df_panel2<- left_join(df_panel2, reg, by= c('State'))


#Number of people repeated in sample

count_reps<- df_panel2 %>% 
        group_by(ID) %>% 
        summarise(n=n())

N_rep<- count_reps %>%
        filter(n > 1)

#  removing ids column
df_panel2<- df_panel2[ -c(1) ]

#converting data frame to panel
df_panel2<- panel_data(df_panel2, id = State, wave = Week)

#Sub-setting the data sample based to remove persons with an income greater than and that haven't been unemployed   
df_panel2 <- subset(df_panel2, income != 7 & income != 8 & wrklossrv != 0 & anywork != 1)

2 Exploratory Data Analysis (EDA)

> #Checking the type of panel balanced or unbalanced
> 
> print('Information about the panel:')
> pdim(df_panel2)
> 
> # Amount of unique values
> print('Amount of unique values:')
> length(unique(df_panel2$ID))
> 
> 
> #Number of people repeated in sample
> print('Number of people repeated in sample: ')
> N_rep
> 
> #Checking panel structure
> #str(df_panel2)
> glimpse(df_panel2)
> xkablesummary(df_panel2, title="Summary Statistics",pos='left', bso= 'condensed')

Evaluating the cross sectional panel, we were able to see that the panel is unbalanced and it has more than 3 million observations. The sample size was reduced when we eliminated the persons that had an income higher than $150,000 and that did not lose employment, given that they would not be the most vulnerable group affected by the pandemic.This reduced the sample size to close to 300 thousand observations. As it was previously mentioned, most of the people in our sample were not interviewed more than three times. Given that the characteristic of cross- sectional data solves normality, rather than focusing in doing normality tests of the data, we focused in understanding it further and the linkages that they seem to be present between variables. Taking a careful look at our data set is relevant so we can better understood the best approach for a regression analysis. The following section highlights some of the key plots

2.1 What can we see from our data?

2.1.1 Findings between sex and state

#** Interviewed sample per sex and state**


theme_set(theme_classic())
ss <- ggplot(df_panel2, aes(x=fct_infreq(State)))
ss + geom_bar(aes(fill=Sex), width = 0.7) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Sample per sex and state", 
       subtitle="Number of persons",
       x= 'States',
       y= 'Frequency')

When analyzing the data we were able to find that California, Texas and Florida had the higher amount of unemployed population, while South Dakota and North Dakota the lowest. Gender distribution seems to be similar across states.

2.1.2 Findings between age and sex

# **Box plot per age and state**


ggplot(aes(x=Sex, y=Age, fill= Sex), data=subset(df_panel2, !is.na(Sex))) + 
   geom_boxplot() +
  stat_summary(fun=mean, geom="point", shape=4)+ 
  labs(title="Box Plot Age/ Sex",
                   x= 'Sex',
                   y= 'Age')+
              theme(legend.position = 'none')

Regarding age and sex, the mean age of males was higher than that of females, with no outliers visible in the dataset.

2.1.3 Findings between education level and region

e1 <- ggplot(data = df_panel2,
            mapping = aes(x = Region, fill = Edu))
e1 + geom_bar(position = "dodge",
             mapping = aes(y = ..prop.., group = Edu))+ 
              labs(title="Levels of education of the sample",
                   subtitle= 'by US region',
                   x= 'Education',
                   y= 'Proportion of the sample')

Per region, the West region shows the higher amount of population with less than high school certificates, while the Northeast shows a higher percentage of persons with graduate degrees. The South and West region seems to have a higher concentration of population. In the Midwest region associate and graduate degree has the highest concentration of population.

2.1.4 Findings replacement ratio by region

replacerate1 <- ggplot(df_panel2, aes(x=Region, y = `Average of Sum of Replacement Ratio 2`, fill = Region)) +
  geom_boxplot() +
  stat_summary(fun=mean, geom="point", shape=4) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        panel.grid.major.y = element_line(color = "lightgray"),
        axis.line = element_line(color = "black"),
        axis.ticks.y=element_blank(),
        axis.ticks.x=element_blank(),
        axis.line.y = element_blank(),
        axis.line.x = element_blank(),
        legend.position = "none") +
  labs(x = "Region", y = "Average of Replacement Ratio ", title = "Average of Replacement Ratio ", subtitle = "By Region")
replacerate1

The replacement rate is higher in the Northeast and West. The higher the replacement rates the more they can alleviate economic hardship. The south region is the one that shows the lowest replace ratio.

2.1.5 Findings race by income

library(ggbeeswarm)


ggplot(df_panel2, aes(y = income, x = Other_race, color = Other_race)) +
  geom_quasirandom(alpha = 0.5) + 
  theme_minimal(base_size = 13) + 
  scale_color_viridis_c(guide = "none") + 
  scale_y_discrete(limits = c(1:6), labels =c('Less than $25,000', '$25,000 - $34,999', '$35,000 - $49,999', '$50,000 - $74,999', '$75,000 - $99,999', '$100,000 - $149,999')) +
  scale_x_discrete(limits = c(1:4), labels =c('White', 'Black', 'Asian', 'Other')) + 
  labs(y = "Income", 
       x = "Race", 
       title = "Household income distribution by largest racial group")

2.1.6 Findings between state and unemployment insurance received

library(gganimate)
#Finding percentage of people that are unemployed and received the benefit
non<-df_panel2[!is.na(df_panel2$UI_recrv),]

t1<-non %>%
  filter(UI_recrv == 1) %>% 
  group_by(Week,State) %>%
  count()
colnames(t1)<- c('State', 'Week','UI')
 

t0<-non %>%
  filter(UI_recrv == 0) %>% 
  group_by(Week,State) %>%
  count()
colnames(t1)<- c('State', 'Week','UIO')

t1_0<- t1 %>% left_join(t0, by= c('State', 'Week'))

t1_0<-t1_0%>% mutate(tot = UIO + n, .after = n)
t1_0<-t1_0%>% mutate(x = UIO /tot, .after = tot)


#Plot
UI_rec <- ggplot(t1_0, 
               aes(x = Week, y= x, color = State)) + 
               geom_point()+
               transition_manual(Week)+
  labs(x = "Weeks", y = "% of unemployed persons receiving unemployment insurance", title = "Change in Unemployment Insurance received by state")
ggplotly(UI_rec)
animate(UI_rec, renderer = gifski_renderer())

anim_save("UI_rec.gif")





#install.packages(c('gapminder','gganimate','gifski'))
#library(gapminder)
#library(gganimate)
#library(gifski)

#ggsave('UI_recieved.png')
#library(gganimate)

#anim_save('UI_rec')
#install.packages("png")
#library(png)
#p1 <- readPNG("UI_recieved.png")
#library(caTools)

#install.packages("magick")
#library(magick)

#write.gif(p1, "UI_recieved.png")
#showGIF <function(fn) system(paste("display",fn))
#showGIF("myPlot.gif")
#unlink("myPlot.gif")

This graph breaks down the percent of unemployment insurance receipt throughout the weeks. Each dot represents the percent of people in the survey for each state that received unemployment Our data begins to captures unemployment insurance being received at week 13. This is likely due to the surge of unemployment claims in the early pandemic causing delays. There is a sharp drop between week 27(March 17 - 29) & week 28 (April 14 - April 26). Beginning the week of April 12, all essential workers in Phase 1C Tier 3 will become eligible for the vaccine.

Sharp decline may have happened due to the expansion of eligibility for the covid vaccine; April 19, 2021 The White House announced that all people age 16 and older are eligible for the COVID-19 vaccine. After may 1 everyone was eligible for the vaccine; while there is no direct tested correlation it’s interesting to point out the decline and the dates of vaccine eligibility
Steady decline after week 40 which was taken after PUA had ended, the recording stops in September of this year where recipients are at their lowest since beginning of data collection.

3 Findings and Conclusions

The amount and frequency of the data in which we are working will allow to be able to avoid statistical mistakes and to be able to obtain a robust regression analysis that could provide relevant insight on unemployment. Having seen how sex and education change through the sample through state and the different percentage of unemployed receiving the benefit, using sex, education, and age as control variables will allow us in the next steps to obtain relevant conclusions that can contribute in informing employment policy in US.

4 Limitations

It is important to mention that although our approach uses data that can represent fairly well the US population under unemployment and receiving unemployment insurance there are still some limitations associated to it:

  • The effects of the unemployment insurance cannot be assessed completely;
  • The way we are qualifying the incentives to return to the job market, could be bias (i.e: health, monetary, etc);
  • Unemployment insurance was not the only incentive that was used during COVID-19 that could descincentivize the return to work (i.e: food stamps, etc);

5 Next Steps

Once the data has been more carefully understood the next steps will required to analyze the type of regression analysis that we would need to undertake. Some of the options can included difference in difference regression, fixed and random effects.Dummy variables will also be selected in order to test difference in intercept terms or slope coefficient, allow the intercept to have different values in each period.

6 References

7 Annex

7.1 Data dictionary

Variable Codes Description
Hispanic

0

1

Hispanic

Non Hispanic
Other Race
  1
  2
  3
  4
White
Black
Asian
Any other race
Education
  1
  2
  3
  4
  5
  6
  7
Less than high school
Some high school
High school grad or equiv
Some college no degree received
Associate’s degree
Bachelor’s degree
Graduate degree
Sex
  0
  1
Female
Male
p_hh
1-40
Number of people in household
wrklossrv

  0
  1
Recent household job loss
No
Yes
anywork

  0
  1
Employment status for last 7 days
No
Yes
kindwork

  1
  2
  3
  4
  5
Sector of Employment
Government
Private company
Non profit
Self-employed
Working in family business
rsnnowrkrv

 1
 2
 3
 4
 5
 6
 7
 8
 9
 10
 11
 12
Main reason for not working
Didn’t want to be employed
Sick or caring for COVID
Caring for children
Caring for an elderly
Concerned getting/spreading COVID
Sick (no COVID)or disabled
Retired
Laid off due to COVID
Employer closed temporarily due to COVID
Employer went out of business due to COVID
Did not have transportation to work
Other reason
UI_apply

  0
  1
UI Apply
No
Yes
UI_recrv

  0
  1
UI Receive
No
Yes
UI_recvnow

  0
  1
 |UI Receive now | No | Yes
house

  0
  1
Housing owned or rented
No
Yes
income

  1
  2
  3
  4
  5
  6
  7
  8
Total household income
Less than $25,000
$25,000 - $34,999
$35,000 - $49,999
$50,000 - $74,999
$75,000 - $99,999
$100,000 - $149,999
$150,000 - $199,999
$200,000 and above
Mean RR 1
Replacement Ratio 1=Weighted Avg of:WBA/(Normal Hourly Wage x 40 Hrs.)
Mean RR 2
Replacement Ratio 2=Ratio of: Weighted Avg WBA/Weighted Avg (Normal Hourly Wage x 40| Hrs.)

7.2 US States per region

Region States
Northeast Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont, New Jersey, New York, Pennsylvania
South Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia, Alabama, Kentucky, Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma, Texas
Midwest Illinois, Indiana, Michigan, Ohio, Wisconsin, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota
West Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming, Alaska, California, Hawaii, Oregon, Washington